PBPK Modeling of Intestinal and Liver Enzymes and Transporters in Drug Absorption and Sequential Metabolism
Bibliographic record
Abstract
Experimental strategies have long been applied for in vitro or in vivo evaluation of the effect of transporters and/or enzymes on the bioavailability. However, the lack of specific inhibitors or inducers of transporters and enzymes and the multiplicity of nuclear receptors in gene regulation and cross-talk have led to compromised assessments of these effects in vivo. These and other causes have resulted in confusion and controversy in transporter-enzyme interplay. In this review, physiologically-based pharmacokinetic (PBPK) intestinal and liver models are utilized to predict the contributions of enzymes and transporters on intestinal availability (FI) and hepatic availability (FH), with the aim to fully understand the impact of these variables on bioavailability (Fsys) in vivo. We emphasize the often overlooked impact of influx and efflux clearances, and apply the PBPK models and their solutions to examine individual organ clearances of the intestine and liver. In order to accurately predict oral bioavailability, these organ models are incorporated into the whole body PBPK model, and additional complicated scenarios such as segmental differences and zonal heterogeneity of transporters and enzymes in the intestine and liver, and segregated blood flow patterns of the intestine are further discussed. The sequential metabolism of a drug to form primary and secondary metabolites in the first-pass organs is considered in PBPK modeling, revealing that the segregated flow model (SFM) of the intestine is more appropriate than the traditional PBPK intestinal model (TM). Examples are included to highlight the potential application of these PBPK models on the quantitative prediction of bioavailability. Keywords: Physiologically-based pharmacokinetic (PBPK) model, bioavailability, enzyme, transporter, liver, intestine, metabolism, excretion, area under the curve (AUC), clearance, PBPK Modeling of Intestinal and Liver Enzymes and Transporters in Drug Absorption and Sequential Metabolism, FH, Fsys, SFM, TM, GI, Lipophilic drugs, carboxylesterases, methyltransferase, P450s, cyclosporine, midazolam, nifedipine, enterocyte, hepatocyte, MCT1, MRP2, BCRP, OATPs, OCT1, BSEP, CAT, ACAT, Vmax, Km, PBPK, FI, Simcyp, Esterase/ enalaprilat, methyldopa sulfate, IVIVE, PLS, ANN, EHBR, availability, Heterogeneity, Zonal Liver Model, area under the curve, ADME, Sulfatase, ENZYMES, M3G, DPD
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".